Risk Management
Operational risk controls for autonomous AI — classification, containment, cognitive SLA, and human escalation infrastructure.
Overview
Risk Management in CGOS operates at runtime — classifying decisions before compute routing, containing anomalies, simulating liability exposure, and escalating to human authority when autonomy conditions fail.
Governance workflows
- Risk classification before compute routing
- Green / Yellow / Red pathway enforcement
- Cognitive SLA and liability simulation hooks
- Model retirement and decommissioning workflows
Runtime supervision
- Drift monitoring and anomaly containment
- Human fatigue and bias monitor signals
- Cross-agent exploit containment
- Autonomy revocation when conditions fail
Enterprise deployment
- Sector-aware risk patterns in onboarding
- Finance and healthcare deployment models
- Enterprise pilot programs with bounded evaluation
- Integration with enterprise GRC workflows
Auditability & evidence
- Risk classification recorded in TAP lineage
- Export paths for model risk reviewers
- Honest limits — awareness, not legal conclusions
- Incident post-review with audit trail export
Operational capabilities
- Runtime risk governance — not static risk registers
- Supervised autonomy with explicit ceilings
- Operational fault management
- Institutional controls for risk officers
Operational boundaries
NerveMind CGOS provides operational governance infrastructure — awareness, traceability, and human authority — not autonomous legal interpretation or certification claims unless explicitly stated in a signed agreement.